OBJECTIVES: The present study was aimed to fabricate the capecitabine as smart pH-responsive hydrogel network to efficiently facilitate its oral delivery while shielding its stability in the gastric media.
METHODS: The smart pH sensitive HP-β-CD/agarose-g-poly(MAA) hydrogel network was developed using an aqueous free radical polymerization technique. The developed hydrogels were characterized for drug-loading efficiency, structural and compositional features, thermal stability, swelling behaviour, morphology, physical form, and release kinetics. The pH-responsive behaviour of developed hydrogels was established by conducting the swelling and release behaviour at different pH values (1.2 and 7.4), demonstrating significantly higher swelling and release at pH 7.4 as compared with pH 1.2. The capecitabine-loaded hydrogels were also screened for acute oral toxicity in animals by analysing the body weight, water and food intake, dermal toxicity, ocular toxicity, biochemical analysis, and histological examination.
RESULTS: The characteristic evaluations revealed that capecitabine (anticancer agent) was successfully loaded into the hydrogel network. Capecitabine loading was ranged from 71.22% to 90.12%. An interesting feature of hydrogel was its pH-responsive behaviour which triggers release at basic pH (94.25%). Optimum swelling (95%) was seen at pH 7.4. Based upon regression coefficient R2 (0.96 - 0.99) best fit model was zero order. The extensive toxicity evaluations evidenced good safety profile with no signs of oral, dermal or ocular toxicities, as well as no variations in blood parameters and histology of vital organs.
CONCLUSION: Our findings conclusively evinced that the developed hydrogel exhibited excellent pharmaceutical and therapeutic potential and thus can be employed as pH-responsive system for controlled delivery of anticancer agents.
METHODS: The warfarin maintenance doses for 140 patients were predicted using the dosing tool and compared with the observed maintenance dose. The impact of genotype was assessed by predicting maintenance doses with prior parameter values known to be altered by genetic variability (eg, EC50 for VKORC1 genotype). The prior population was evaluated by fitting the published kinetic-pharmacodynamic model, which underpins the Bayesian tool, to the observed data using NONMEM and comparing the model parameter estimates with published values.
RESULTS: The Bayesian tool produced positively biased dose predictions in the new cohort of patients (mean prediction error [95% confidence interval]; 0.32 mg/d [0.14-0.5]). The bias was only observed in patients requiring ≥7 mg/d. The direction and magnitude of the observed bias was not influenced by genotype. The prior model provided a good fit to our data, which suggests that the bias was not caused by different prior and posterior populations.
CONCLUSIONS: Maintenance doses for patients requiring ≥7 mg/d were overpredicted. The bias was not due to the influence of genotype nor was it related to differences between the prior and posterior populations. There is a need for a more mechanistic model that captures warfarin dose-response relationship at higher warfarin doses.